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Turbomachinery Design

Key Takeaways

  • Challenges such as flow instability, energy loss, and pressure drop affect the performance of turbomachinery. 

  • Turbomachinery design optimization seeks to achieve excellent performance, reliability in operation, and cost efficiency. 

  • Different CFD-based optimization techniques facilitate the identification of design parameters and their relationship, which is crucial for analyzing the performance and efficiency of the turbomachinery design. 

Analyzing turbomachinery design with CFD

The importance of efficiency cannot be overstated, especially in recent times when almost every industry seeks to be more productive while also being environmentally sustainable. Turbomachinery applications face the challenges of flow instability, energy loss, pressure drop, flow separation, etc., making them less effective. At the same time, many turbomachines still use fossil fuels to operate. Cumulatively, less work is done with more energy consumption. 

With turbomachinery design optimization, engineers aim to make machines more efficient in all aspects. With an optimal flow path and ideal design, turbomachinery can produce more power, have a longer operating span, and have a smaller environmental footprint. To achieve this, there are many optimization techniques that can be put to use. In this article, we will discuss the different techniques for turbomachinery design optimization. 

Turbomachinery Design Optimization 

Why is optimization required? Turbomachinery is a complex system in terms of how it is built and operates. The interaction between the fluid, its thermal properties, and the design of the machine makes it challenging for turbomachinery process solutions to meet all performance requirements. 

Optimization Benefits

Performance efficiency

An optimized turbomachinery design will achieve the desired flow rate, outlet and inlet pressure, and power output. 

Operational reliability

Optimizing the design ensures the turbomachinery will operate reliably with minimal downtime and minimal risk of failure due to issues such as cavitation.


Optimal design minimizes costs related to material and manufacturing while providing high-rated performance. This also includes less energy consumption by the machine and the minimization of costs required for repairs and maintenance. 

Optimization Techniques

To achieve the best possible performance from a turbomachinery design, engineers and system designers may use a range of techniques, including:

 turbomachinery design optimization

  1. Design of experiments (DOE): This statistical technique can be used for identifying the optimal design parameters. The DOE approach systematically alters the input parameters and measures the output using a set of experimental designs. Within the design space, the DOE facilitates the identification of the most important design parameters, the interaction between them, and their effect on the performance of the turbomachinery. 
  2. Response surface method (RSM): This technique establishes a mathematical relation between the design variables (such as the shape and size of the internal components) and the performance of the turbomachinery. This technique uses the DOE approach to develop a set of experimental designs and evaluate the responses. The results can be used to develop a response surface, which is a model that facilitates the prediction of the performance. For any set of input parameters within the range of experimental data, the best possible output can be identified, allowing for efficient design optimization.  
  3. Gradient-based optimization: In this technique, the gradient of the objective function is calculated using techniques such as finite difference to identify the most important design parameters (such as flow path, blade angle, shape, and size). The objective function is any performance metric, such as efficiency, pressure, or power, that is in need of optimization. Once the gradient is calculated and key parameters identified, these design parameters can be updated until the optimal design is obtained. 
  4. Genetic algorithm: This is a population-based optimization technique where the design space is selected randomly and explored until the ideal design is identified. Multiple and large-scale design spaces can be explored efficiently, facilitating the identification of the optimal design solution with minimal loss and an optimal design profile. 
  5. Multi-objective optimization: This approach is suitable when more than one parameter of a conflicting nature needs to be optimized. For example, when performance needs to be maximized but structural weight or cost has to be minimized. This optimization technique uses several approaches, such as the Pareto front, weighted sum, or constraint method, to identify the optimal trade-off points for theparameters such that the turbomachinery design meets all performance criteria. 

These optimization techniques can leverage CFD simulation to further simplify data analysis and the design improvement process. 

CFD Simulation for Turbomachinery Design Optimization

Computational fluid dynamics (CFD) tools are suitable for implementing these optimization techniques for a refined result. Engineers can use CFD tools to construct models, generate data points, simulate and analyze flow behavior, and identify design variables. The relationship between these parameters can then be studied to analyze the performance and efficiency of the turbomachinery design. This process is made easier with tools like Fidelity and Fidelity Pointwise from Cadence. By implementing these CFD-based optimization techniques, the optimal turbomachinery design can be identified. 

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About the Author

With an industry-leading meshing approach and a robust host of solver and post-processing capabilities, Cadence Fidelity provides a comprehensive Computational Fluid Dynamics (CFD) workflow for applications including propulsion, aerodynamics, hydrodynamics, and combustion.

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